InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks
Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often...
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Elsevier
2025-02-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025008564 |
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| author | Mahmood Kalemati Mojtaba Zamani Emani Somayyeh Koohi |
| author_facet | Mahmood Kalemati Mojtaba Zamani Emani Somayyeh Koohi |
| author_sort | Mahmood Kalemati |
| collection | DOAJ |
| description | Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often overlooks essential biological context features, hindering effective binding prediction. Additionally, these models struggle to capture global and local feature distributions efficiently in protein sequences and drug SMILES. Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. Transformers struggle with scalability, while graph-based methods have difficulty handling large datasets and complex molecular structures. In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. We evaluate InceptionDTA across a range of benchmark datasets commonly used in drug-target binding affinity prediction. Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. In addition to using CharVec, which demonstrates greater accuracy in absolute predictions, InceptionDTA also includes a version that employs simple label encoding and excels in ranking and predicting relative binding affinities. This versatility highlights how InceptionDTA can effectively adapt to various predictive requirements. These results emphasize the promise of our approach in expediting drug repurposing initiatives, enabling the discovery of new drugs, and contributing to advancements in disease treatment. |
| format | Article |
| id | doaj-art-78f02d90827b4d81aa8c93eacf96fb03 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-78f02d90827b4d81aa8c93eacf96fb032025-02-09T05:00:42ZengElsevierHeliyon2405-84402025-02-01113e42476InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networksMahmood Kalemati0Mojtaba Zamani Emani1Somayyeh Koohi2Department of Computer Engineering, Sharif University of Technology, Tehran, IranDepartment of Computer Engineering, Sharif University of Technology, Tehran, IranCorresponding author.; Department of Computer Engineering, Sharif University of Technology, Tehran, IranPredicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often overlooks essential biological context features, hindering effective binding prediction. Additionally, these models struggle to capture global and local feature distributions efficiently in protein sequences and drug SMILES. Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. Transformers struggle with scalability, while graph-based methods have difficulty handling large datasets and complex molecular structures. In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. We evaluate InceptionDTA across a range of benchmark datasets commonly used in drug-target binding affinity prediction. Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. In addition to using CharVec, which demonstrates greater accuracy in absolute predictions, InceptionDTA also includes a version that employs simple label encoding and excels in ranking and predicting relative binding affinities. This versatility highlights how InceptionDTA can effectively adapt to various predictive requirements. These results emphasize the promise of our approach in expediting drug repurposing initiatives, enabling the discovery of new drugs, and contributing to advancements in disease treatment.http://www.sciencedirect.com/science/article/pii/S2405844025008564Drug-target binding affinity predictionDeep representation learningInception networkInteractionCharVec encoding |
| spellingShingle | Mahmood Kalemati Mojtaba Zamani Emani Somayyeh Koohi InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks Heliyon Drug-target binding affinity prediction Deep representation learning Inception network Interaction CharVec encoding |
| title | InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks |
| title_full | InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks |
| title_fullStr | InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks |
| title_full_unstemmed | InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks |
| title_short | InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks |
| title_sort | inceptiondta predicting drug target binding affinity with biological context features and inception networks |
| topic | Drug-target binding affinity prediction Deep representation learning Inception network Interaction CharVec encoding |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025008564 |
| work_keys_str_mv | AT mahmoodkalemati inceptiondtapredictingdrugtargetbindingaffinitywithbiologicalcontextfeaturesandinceptionnetworks AT mojtabazamaniemani inceptiondtapredictingdrugtargetbindingaffinitywithbiologicalcontextfeaturesandinceptionnetworks AT somayyehkoohi inceptiondtapredictingdrugtargetbindingaffinitywithbiologicalcontextfeaturesandinceptionnetworks |